Average F-measure (\(AF_\beta \) ):
To emphasize the precision over recall \(r_T\) , the weight \(\beta ^2\)  should be chosen less than 1. Especially, when \(\beta ^2=1\) , \(F_\beta \)  becomes the standard F-measure with equal weighting on the precision and recall [1]}, which can be expressed as \(F_1=2\times IoU/(1+IoU)\)  and considered as mathematically alternative to the Intersection-over-Union (IoU) metric itself [2]}. As recommended in [3]}, coefficient \(\beta ^2\)  could be selected at 0.3. On other hand, \(\beta ^2\)  = 0.25 is also frequently used to evaluate the quality of image processing [4]}. However, no strong evidence is demonstrated in the literature to prove the priority of 0.25 or 0.3 among other adjacent values. For a fairer comparison through the F-measure, we propose a new evaluation metric to calculate the average F-measure \(AF_\beta \)  over a given range of \(\beta ^2\) . Here, the average F-measure, \(AF_\beta \) , is formulated by:
\(AF_\beta = \frac{1}{\beta ^2_2-\beta ^2_1}\int _{\beta ^2_1}^{\beta ^2_2} F_\beta d\beta ^2,\) 
